SoroushVahidi/scidocs huggingface dataset:
Nonlinear controller design for a magnetic levitation device
(2007)

Various applications of micro-robotic technology suggest the use of new actuator systems which allow motions to be realized with micrometer accuracy. Conventional actuation techniques such as hydraulic or pneumatic systems are no longer capable of fulfilling the demands of hi-tech micro-scale areas such as miniaturized biomedical devices and MEMS production equipment. These applications pose significantly different problems from actuation on a large scale. In particular, large scale manipulation systems typically deal with sizable friction, whereas micro manipulation systems must minimize friction to achieve submicron precision and avoid generation of static electric fields. Recently, the magnetic levitation technique has been shown to be a feasible actuation method for microscale applications. In this paper, a magnetic levitation device is recalled from the authors’ previous work and a control approach is presented to achieve precise motion control of a magnetically levitated object with sub-micron positioning accuracy. The stability of the controller is discussed through the Lyapunov method. Experiments are conducted and showed that the proposed control technique is capable of performing a positioning operation with rms accuracy of 16 lm over a travel range of 30 mm. The nonlinear control strategy proposed in this paper showed a significant improvement in comparison with the conventional control strategies for large gap magnetic levitation systems.
Treatment planning of the edentulous maxilla
(2006)

The predictability of successful osseointegrated implant rehabilitation of the edentulous jaw as described by Branemark et al., introduced a new era of management for the edentulous predicament. Implant rehabilitation of the edentulous maxilla remains one of the most complex restorative challenges because of the number of variables that affect both the aesthetic and functional aspect of the prosthesis. Among the prosthesis designs used to treat the edentulous maxilla are fixed or removable implant-supported restorations. Since the aesthetic requirements and preoperative situation of each patient varies, considerable time must be spent on accurate diagnosis to ensure patient desires are satisfied and predictable outcomes are achieved. The purpose of this article is to compare the treatment options and prosthesis designs for the edentulous maxilla. Emphasis will be placed on diagnosis and treatment planning. Criteria will be given to guide the practitioner in deciding whether a fixed or removable restoration should be placed. This objective will be accomplished through the review of cases with regard to varying design considerations and factors that influence the decision-making process.
An Efficient Diagnosis System for Parkinson's Disease Using Kernel-Based Extreme Learning Machine with Subtractive Clustering Features Weighting Approach
(2014)

A novel hybrid method named SCFW-KELM, which integrates effective subtractive clustering features weighting and a fast classifier kernel-based extreme learning machine (KELM), has been introduced for the diagnosis of PD. In the proposed method, SCFW is used as a data preprocessing tool, which aims at decreasing the variance in features of the PD dataset, in order to further improve the diagnostic accuracy of the KELM classifier. The impact of the type of kernel functions on the performance of KELM has been investigated in detail. The efficiency and effectiveness of the proposed method have been rigorously evaluated against the PD dataset in terms of classification accuracy, sensitivity, specificity, area under the receiver operating characteristic (ROC) curve (AUC), f-measure, and kappa statistics value. Experimental results have demonstrated that the proposed SCFW-KELM significantly outperforms SVM-based, KNN-based, and ELM-based approaches and other methods in the literature and achieved highest classification results reported so far via 10-fold cross validation scheme, with the classification accuracy of 99.49%, the sensitivity of 100%, the specificity of 99.39%, AUC of 99.69%, the f-measure value of 0.9964, and kappa value of 0.9867. Promisingly, the proposed method might serve as a new candidate of powerful methods for the diagnosis of PD with excellent performance.
Speaker-listener neural coupling underlies successful communication.
(2010)

Verbal communication is a joint activity; however, speech production and comprehension have primarily been analyzed as independent processes within the boundaries of individual brains. Here, we applied fMRI to record brain activity from both speakers and listeners during natural verbal communication. We used the speaker's spatiotemporal brain activity to model listeners' brain activity and found that the speaker's activity is spatially and temporally coupled with the listener's activity. This coupling vanishes when participants fail to communicate. Moreover, though on average the listener's brain activity mirrors the speaker's activity with a delay, we also find areas that exhibit predictive anticipatory responses. We connected the extent of neural coupling to a quantitative measure of story comprehension and find that the greater the anticipatory speaker-listener coupling, the greater the understanding. We argue that the observed alignment of production- and comprehension-based processes serves as a mechanism by which brains convey information.
Practical Signal-Dependent Noise Parameter Estimation From a Single Noisy Image
(2014)

The additive white Gaussian noise is widely assumed in many image processing algorithms. However, in the real world, the noise from actual cameras is better modeled as signal-dependent noise (SDN). In this paper, we focus on the SDN model and propose an algorithm to automatically estimate its parameters from a single noisy image. The proposed algorithm identifies the noise level function of signal-dependent noise assuming the generalized signal-dependent noise model and is also applicable to the Poisson-Gaussian noise model. The accuracy is achieved by improved estimation of local mean and local noise variance from the selected low-rank patches. We evaluate the proposed algorithm with both synthetic and real noisy images. Experiments demonstrate that the proposed estimation algorithm outperforms the state-of-the-art methods.
Human-Assisted Graph Search: It's Okay to Ask Questions
(2011)

We consider the problem of human-assisted graph search: given a directed acyclic graph with some (unknown) target node(s), we consider the problem of finding the target node(s) by asking an omniscient human questions of the form “Is there a target node that is reachable from the current node?”. This general problem has applications in many domains that can utilize human intelligence, including curation of hierarchies, debugging workflows, image segmentation and categorization, interactive search and filter synthesis. To our knowledge, this work provides the first formal algorithmic study of the optimization of human computation for this problem. We study various dimensions of the problem space, providing algorithms and complexity results. We also compare the performance of our algorithm against other algorithms, for the problem of webpage categorization on a real taxonomy. Our framework and algorithms can be used in the design of an optimizer for crowdsourcing platforms such as Mechanical Turk.
Joint detection and decoding for MIMO systems with polar codes
(2016)

As well known, the near-optimal K-best detection is popular in multiple-input and multiple-output (MIMO) systems. In this paper, we first propose the joint approaches of K-best detection and polar decoding. For joint detection and decoding (JDD) approach, both hard and soft decisions are considered. The simplified successive cancellation (SSC) decoding is exploited for hard decision, and the successive cancellation list (SCL) decoding is used as soft decision. The system setup for JDD is als o introduced, in which the modulation points across several channels are considered together. Simulation results have demonstrated the performance advantage of the JDD algorithms over the separated ones. For 1/2-rate polar codes, JDD schemes show 50% complexity reduction compared to the separated ones. Furthermore, by employing SSC hard decoding, the JDD algorithm is promising for high-throughput and low-complexity application s.
The Alignment Template Approach to Statistical Machine Translation
(2004)

A phrase-based statistical machine translation approach the alignment template approach is described. This translation approach allows for general many-to-many relations between words. Thereby, the context of words is taken into account in the translation model, and local changes in word order from source to target language can be learned explicitly. The model is described using a log-linear modeling approach, which is a generalization of the often used source-channel approach. Thereby, the model is easier to extend than classical statistical machine translation systems. We describe in detail the process for learning phrasal translations, the feature functions used, and the search algorithm. The evaluation of this approach is performed on three different tasks. For the German-English speech Verbmobil task, we analyze the effect of various system components. On the French-English Canadian Hansards task, the alignment template system obtains significantly better results than a single-word-based translation model. In the Chinese-English 2002 National Institute of Standards and Technology (NIST) machine translation evaluation it yields statistically significantly better NIST scores than all competing research and commercial translation systems.
Adolescent Internet Addiction: Testing the Association Between Self-Esteem, the Perception of Internet Attributes, and Preference for Online Social Interactions
(2012)

There is a general consensus that Internet addiction (IA) is mainly related to social aspects of the Web, especially among adolescents. The empirical link between poor social skills and IA is well documented; however, theoretical explanations for this relationship are less developed. One possibility is that people with poor social skills are especially prone to develop a preference for online social interaction (POSI), which, in turn, predicts problematic usage. This hypothesis has been tested for loneliness and social anxiety, but not for self-esteem (SE; one of the main antecedents of IA). Furthermore, the mediating role of the perceived relevance of some Internet features (e.g., anonymity) in the relationship between SE and POSI has never been investigated. A cross-sectional study was conducted with 257 adolescents. Using mediation analyses, we found evidence among females for the mediating role of (a) POSI in the relationship between SE and IA, and (b) the subjective relevance of some Internet features in the association between SE and POSI. No significant effects were found for males.
Automatic Detection of Calibration Grids in Time-of-Flight Images
(2014)

It is convenient to calibrate time-of-flight cameras by established methods, using images of a chequerboard pattern. The low resolution of the amplitude image, however, makes it difficult to detect the board reliably. Heuristic detection methods, based on connected image-components, perform very poorly on this data. An alternative, geometrically-principled method is introduced here, based on the Hough transform. The projection of a chequerboard is represented by two pencils of lines, which are identified as oriented clusters in the gradient-data of the image. A projective Hough transform is applied to each of the two clusters, in axis-aligned coordinates. The range of each transform is properly bounded, because the corresponding gradient vectors are approximately parallel. Each of the two transforms contains a series of collinear peaks; one for every line in the given pencil. This pattern is easily detected, by sweeping a dual line through the transform. The proposed Hough-based method is compared to the standard OpenCV detection routine, by application to several hundred time-of-flight images. It is shown that the new method detects significantly more calibration boards, over a greater variety of poses, without any overall loss of accuracy. This conclusion is based on an analysis of both geometric and photometric error.
Reinforcement Learning with Self-Modifying Policies
(1998)

A learner’s modifiable components are called its policy. An algorithm that modifies the policy is a learning algorithm. If the learning algorithm has modifiable components represented as part of the policy, then we speak of a self-modifying policy (SMP). SMPs can modify the way they modify themselves etc. They are of interest in situations where the initial learning algorithm itself can be improved by experience — this is what we call “learning to learn”. How can we force some (stochastic) SMP to trigger better and better self-modifications? The success-story algorithm (SSA) addresses this question in a lifelong reinforcement learning context. During the learner’s life-time, SSA is occasionally called at times computed according to SMP itself. SSA uses backtracking to undo those SMP-generated SMP-modifications that have not been empirically observed to trigger lifelong reward accelerations (measured up until the current SSA call — this evaluates the long-term effects of SMP-modifications setting the stage for later SMP-modifications). SMP-modifications that survive SSA represent a lifelong success history. Until the next SSA call, they build the basis for additional SMP-modifications. Solely by self-modifications our SMP/SSA-based learners solve a complex task in a partially observable environment (POE) whose state space is far bigger than most reported in the POE literature.
Speaker-sensitive dual memory networks for multi-turn slot tagging
(2017)

In multi-turn dialogs, natural language understanding models can introduce obvious errors by being blind to contextual information. To incorporate dialog history, we present a neural architecture with Speaker-Sensitive Dual Memory Networks which encode utterances differently depending on the speaker. This addresses the different extents of information available to the system — the system knows only the surface form of user utterances while it has the exact semantics of system output. We performed experiments on real user data from Microsoft Cortana, a commercial personal assistant. The result showed a significant performance improvement over the state-of-the-art slot tagging models using contextual information.
Integrated force and distance sensing using elastomer-embedded commodity proximity sensors
(2016)

We describe a combined force and distance sensor using a commodity infrared distance sensor embedded in a transparent elastomer with applications in robotic manipulation. Prior to contact, the sensor works as a distance sensor (0–10 cm), whereas after contact the material doubles as a spring, with force proportional to the compression of the elastomer (0– 5 N). We describe its principle of operation and design parameters, including polymer thickness, mixing ratio, and emitter current, and show that the sensor response has an inflection point at contact that is independent of an object’s surface properties. We then demonstrate how two arrays of eight sensors, each mounted on a standard Baxter gripper, can be used to (1) improve gripper alignment during grasping, (2) determine contact points with objects, and (3) obtain crude 3D models that can serve to determine possible grasp locations.
Accurate Synchronization of Speech and EGG Signal Using Phase Information
(2017)

Synchronization of speech and corresponding Electroglottographic (EGG) signal is very helpful for speech processing research and development. During simultaneous recording of speech and EGG signals, the speech signal will be delayed by the duration corresponding to the speech wave propagation from the glottis to the microphone relative to the EGG signal. Even in same session of recording, the delay between the speech and the EGG signals is varying due to the natural movement of speaker’s head and movement of microphone in case MIC is held by hand. To study and model the information within glottal cycles, precise synchronization of speech and EGG signals is of utmost necessity. In this work, we propose a method for synchronization of speech and EGG signals based on the glottal activity information present in the signals. The performance of the proposed method is demonstrated by estimation of delay between the two signals (speech signals and corresponding EGG signals) and synchronizing these signals by compensating the estimated delay. The CMU-Arctic database consist of simultaneous recording of the speech and the EGG signals is used for the evaluation of the proposed method.
Wormhole Attack Detection in Wireless Networks
(2016)

Wormhole attacks can destabilize or disable wireless sensor networks. In a typical wormhole attack, the attacker receives packets at one point in the network, forwards them through a wired or wireless link with less latency than the network links, and relays them to another point in the network. We first propose a centralized algorithm to detect wormholes and show its correctness rigorously. For the distributed wireless network, we proposes DAWN, Distributed detection Algorithm against Wormhole in wireless Network coding systems, by exploring the change of the flow directions of the innovative packets caused by wormholes. We find that the robustness depends on the node density in the network, and prove a necessary condition to achieve collision-resistance. Our solutions only depend on the local information that can be obtained from regular network coding protocols, and thus the overhead that our algorithms introduce is acceptable for most applications.
Polygraph: automatically generating signatures for polymorphic worms
(2005)

It is widely believed that content-signature-based intrusion detection systems (IDS) are easily evaded by polymorphic worms, which vary their payload on every infection attempt. In this paper, we present Polygraph, a signature generation system that successfully produces signatures that match polymorphic worms. Polygraph generates signatures that consist of multiple disjoint content substrings. In doing so, Polygraph leverages our insight that for a real-world exploit to function properly, multiple invariant substrings must often be present in all variants of a payload; these substrings typically correspond to protocol framing, return addresses, and in some cases, poorly obfuscated code. We contribute a definition of the polymorphic signature generation problem; propose classes of signature suited for matching polymorphic worm payloads; and present algorithms for automatic generation of signatures in these classes. Our evaluation of these algorithms on a range of polymorphic worms demonstrates that Polygraph produces signatures for polymorphic worms that exhibit low false negatives and false positives.
Are We Ready for Driver-less Vehicles? Security vs. Privacy- A Social Perspective
(2014)

At this moment Automous cars are probably the biggest and most talked about technology in the Robotics Reserach Community. In spite of great technolgical advances over past few years a full fledged autonomous car is still far from reality. This article talks about the existing system and discusses the possibility of a Computer Vision enabled driving being superior than the LiDar based system. A detailed overview of privacy violations that might arise from autonomous driving has been discussed in detail both from a technical as well as legal perspective. It has been proved through evidence and arguments that efficient and accurate estimation and efficient solution of the contraint satisfaction problem adressed in the case of autonomous cars are negatively correlated with the preserving the privacy of the user. It is a very difficult trade-off since both are very important aspects and has to be taken into account. The fact that one cannot compromise with the safety issues of the car makes it inevidable to run into serious privacy concerns that might have adverse social and political effects.
Image-based Localization with Spatial LSTMs
(2016)

In this work we propose a new CNN+LSTM architecture for camera pose regression for indoor and outdoor scenes. CNNs allow us to learn suitable feature representations for localization that are robust against motion blur and illumination changes. We make use of LSTM units on the CNN output in spatial coordinates in order to capture contextual information. This substantially enlarges the receptive field of each pixel leading to drastic improvements in localization performance. We provide extensive quantitative comparison of CNN-based vs SIFT-based localization methods, showing the weaknesses and strengths of each. Furthermore, we present a new large-scale indoor dataset with accurate ground truth from a laser scanner. Experimental results on both indoor and outdoor public datasets show our method outperforms existing deep architectures, and can localize images in hard conditions, e.g., in the presence of mostly textureless surfaces.
Communication issues in requirements elicitation: a content analysis of stakeholder experiences
(2003)

The gathering of stakeholder requirements comprises an early, but continuous and highly critical stage in system development. This phase in development is subject to a large degree of error, influenced by key factors rooted in communication problems. This pilot study builds upon an existing theory-based categorisation of these problems through presentation of a four-dimensional framework on communication. Its structure is validated through a content analysis of interview data, from which themes emerge, that can be assigned to the dimensional categories, highlighting any problematic areas. The paper concludes with a discussion on the utilisation of the framework for requirements elicitation exercises.
Game Theory in Signal Processing and Communications
(2009)

1Department of Mobile Communications, School of Electrical Engineering and Computer Sciences, Technical University of Berlin, Berlin, Germany 2Wireless Networking, Signal Processing and Security Lab, Department of Electrical and Computer Engineering, University of Houston, Houston, TX 77004, USA 3Division of Communication Systems, Department of Electrical Engineering (ISY), Linköping University, SE-581 83 Linköping, Sweden 4Communications Laboratory, Faculty of Electrical Engineering and Information Technology, Dresden University of Technology, 01062 Dresden, Germany
Exercise motion classification from large-scale wearable sensor data using convolutional neural networks
(2017)

The ability to accurately identify human activities is essential for developing automatic rehabilitation and sports training systems. In this paper, large-scale exercise motion data obtained from a forearm-worn wearable sensor are classified with a convolutional neural network (CNN). Time-series data consisting of accelerometer and orientation measurements are formatted as images, allowing the CNN to automatically extract discriminative features. A comparative study on the effects of image formatting and different CNN architectures is also presented. The best performing configuration classifies 50 gym exercises with 92.1% accuracy.